Gb Training Dmaic

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GB TRG MATERIAL Slide 1 Proprietary to Wipro Ltd

D M A I C

Six SigmaGreen Belt TRAINING


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How Successful Leaders see Six Sigma

Six Sigma is the most important initiative GE hasever takenit is part of the genetic code of our future leadership.

-Jack Welch, Former CEO,General Electric

You can see the before & after of an organizationwhen Six Sigma grabs hold & takes place.

-Richard Johnson, Director of Six Sigma,Allied Signal

Successful leaders have made Six Sigma their way of conducting business.-Azim Premji, Chairman,Wipro Limited


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What is Six Sigma?

Six Sigma as a metric

At Six Sigma level, a process produces less than 3.4 defects in a million or

99.99966% Yield

Six Sigma as a methodology

Improving process capability to desired levels

Six Sigma as a philosophy

Business excellence through organizational transformation

Six Sigma : Variation Based thinking


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6 Sigma as a metric

Which process is performing best?

PROCESS PERFORMANCE

Call servicing 32 seconds ASA vs. goal of 35

Billing 98% accuracy

Accounts Receivable 33 days average aging vs. goal of 40

Customer Service 82% rated 4 or 5 on responsiveness

The Sigma Scale provides a common scale/ metric for comparison.

Sigma level DPMO

2 308,5373 66,8074 6,2105 233

6 3.4


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An efficient & effective method :

Six Sigma is a structured application of process improvement tools,

applied by dedicated resources on a project basis,

to increase customer satisfaction and reach strategic business goals.

.

Six Sigma: As Methodology

BusinessProblem StatisticalProblem StatisticalSolution BusinessSolution

Black BeltsExpertise

StatisticalTools

Black BeltsExpertise

P R O J E C T


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A philosophy :

For many companies, Six Sigma is a management philosophy.

Six Sigma: As Philosophy

Successful leaders have made Six Sigma their way of conducting business .-Azim Premji, Chairman,Wipro Limited

Six sigma is the best training that an organization can give to its employees,

its even better than sending them to Harward Business School. - Jack Welch, Former CEO, GE


7/161GB TRG MATERIAL Slide 7 Proprietary to Wipro Ltd

Six Sigma is Variance Based Thinking

-20 -10 0 10 20

Consider below performance on On time delivery

Target 0 days

Mean 0.70 days

On Target !!!! Good Performance ??



Six Sigma is Variance Based Thinking

-20 -10 0 10 20

What customer sees!!

Customer always looks at , mean is meaninglessvariance



Are We Looking at the Variation?

Dont worry,That rope is

one inch thickon average.

If I had to reduce my message to management to just a few words, Id say it all had to do with

reducing variation W. Edwards Deming



DMAIC Overview

DEFINEThe Problem

MEASUREY ( Outcome)

Validate Measurement System

ANALYZEBaseline Y and Set Goal

Identify Xs(Variation Sources)

IMPROVEQuantify Xs

CONTROLControl Xs

DMAIC applies to an existing process

that needs improvement



DMAIC

Establish CTQ Characteristics



CTQ (Critical to Quality)

A CTQ is a

Product or Service

characteristic

that satisfies a

Customer Requirement

OR

Process Requirement

Business CTQ

Customer CTQ

Internal CTQ

Project CTQ



What are Business CTQs?

Business CTQs drive the business goals & vision

Typical Business CTQs are in the area of:

Operational Excellence

Cost Reduction

Productivity Improvement

Employee Satisfaction

Customer Satisfaction

Sales Growth

Profitability



Define your Customer



Who is your customer?

A customer is someone who

Uses your product or service

Decides to buy your product or service

Pays for your product or service

Gets impacted by your product or service

Internal & External customers

Primary & Secondary customers

Key concepts

Different markets / segments

Different customer requirements

Product / Service chain



Explore Customer CTQ



Customer CTQs

Six Sigma begins with the customer

Customers find it easier to define what they do not want

Customer CTQs are defined by customers

Sources of customer CTQs

Survey results

Service reviews

Meetings



Customer Requirement perceived by Sales Team



Customer Requirement captured by Design Team



Customer Requirement created by Production Team



Customer Requirement delivered by Implementation Team



Actual Customer Requirement


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How to Explore Customer CTQs

Often, customer requirements are hazy

Customer requirements must be understood clearly

VOC is a technique to organize, analyze & profile the customer requirements

(collection of requirements is done through surveys / meetings)

VOC Table


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VOC Data Source

REACTIVE SOURCES

InterviewsFocus groups

Surveys

Comment cards

Sales visits/calls

Direct observation

Market research/monitoring

Benchmarking

Quality scorecards

PROACTIVE SOURCES

ComplaintsProblem or service hotlines

Technical support calls

Customer service calls

Claims, credits

Sales reporting

Product return information

Warranty claims

Web page activity

NPS survey outcomes can be Reactive & Proactive source


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Define Internal CTQ / CBP


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How to Arrive at Internal CTQs / CBPs

Customer CTQs( needs) are often hazy & they must be converted into meaningfulinternal goals that are assignable to functions

More structured methods & tools are available to convert Customer CTQs /

Business CTQs into internal CTQs / CBPs

CTQ Drill-down

Quality Function Deployment (QFD / House of Quality)


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Example CTQ Drill Down

ImproveProfitability

Reducingcost

Manpower cost

Productivity Salary cost

Travel &overheads Infrastr. cost

IncreaseRealization

or Billing rate

New Geos High EndPractices

OffshoreBusiness

Model

MoreOffshore

component

Productivity (NLD) Projects were selected from the BusinessCTQ of Profitability


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One Example of A Typical CTQ Drill-down

Example of On-time delivery CTQ

Total Units 168

Orders Fully Serviced 132 Orders Not Fully Serviced 36A 36

6 Late

52 Defects 36 Defects

B 47

31 late

C 31

8 late

D 18

7 Late

A 4 B 27 C 1 D 4

With the data support, team decided to take project inProduct B, Which is contributing to more than 70% defects


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DMAIC

Define a Project


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Theme Selection Matrix

Theme selection matrix can be used to prioritize project themes coming from thebrainstorming exercise

It is a structured & data-oriented prioritization exercise to select right project

A scale of 1-9 is applied to evaluate multiple themes on four aspects. Final score isarrived by multiplying the scores of each category

Importance to the internal customer

Importance to the External customer

Financial benefits/ Annum Effort

Final score

6

4

3

CTQ type

Rating Parameters for the themes (1-3-5-7-9)

S.no. Selected Themes

1

5

2


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Scope the Project


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What is Scoping?

Its an attempt to define what will be covered in the project deliverables

Scoping sharpens the focus of the project team

Sets the expectations right

Types of Scoping

Longitudinal Scoping

Lateral Scoping


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Longitudinal Scoping

Longitudinal scoping is done on the length of the process

e.g. From the time a bug is assigned to the time it is submitted for fix

e.g. From the time of customer reporting the complaint till final satisfaction confirmation

Mostly the start & end points are baton change points

A macro as-is process map must be prepared to facilitate longitudinal scoping


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Example of a Photocopying Process


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Example - SIPOC

Suppliers Inputs Process Outputs Customers

End User Mail Receipt of Mailfrom User

AcknowledgementMail End User

Wipro MIT Helpdesk TeamCall allocation tothe HelpdeskExecutive

Client IT Dept IT infrastructure

HelpdeskExecutive Logs theTicket On E-helpline*HelpDesk sendsacknowledgementto the Customer

Process NameStarting point: User sending a mail to HelpdeskEnd point: Call logging in the Tool/Email Response to the Customer

l


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Process Mapping Nomenclature

Process Decision Data

Pre-definedProcess

Document Terminator

ManualOperation

DelayManual

Input

l


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Lateral Scoping

Lateral scoping is done on the breadth of the process

e.g. All despatches from North & South regions

e.g. Calls received during general shift

One or more of the following are covered here:

What all kinds of units the process will cover

In what situations the process is valid

What are the qualifiers for the transactions

What functional domains does the process cover

In what geographical areas the process is valid

CTQ Drill down and pareto analysis will help in effective lateral scoping


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Quantify Benefits

P j i f B fi


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Projection of Benefits

Six Sigma has a strong focus on money

Management buy-in is easier for tangible benefit projects

Tangible benefits could be of various types

Cost Reduction / Saving

Increase in Sales / Revenue

Enhanced productivity

Enhanced measurable Customer Satisfaction

Enhanced measurable Employee Satisfaction


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DEFINE THE PROBLEM STATEMENT

D fi i P bl St t t


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Defining a Problem Statement

In the last 3 months, 12% of our customers are late, by over 45 days in

paying their bills. This represents 20% of our outstanding receivables &

negatively affects our operating cash flow.

Our customers are angry with us and thus delay paying their bills.

What

When

MagnitudeConsequence


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D

M AIC

EstablishPerformanceParameters


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Map Detailed As-is Process

Wh t d h l it ?


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What do you see hereany clarity?

S th Diff N


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See the Difference Now

Process Mapping is a graphic display of steps & activities that constitute a process

Recall..Onepicture is

better than1000 words

B fit f P M i g


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Benefits of Process Mapping

Simple & visible structure for thinking through a complex process

Enables seeing the entire process as a team

Enables seeing that changes are not made in a vacuum and will carry through,

affecting the entire process down the line

Magnifies non value-added areas or steps

Identifies cycle times of each step in the process

Helps re-examine (if needed) the scope and charter of your project

Micro Process Map of a Manufacturing Process


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Micro Process Map of a Manufacturing Process

continuousuncoiling of coil

degreasingconcn-0.3-0.4temp-80 Deg

hotwater rinsingtemp=80 Deg C

cold (roomtemp)water rinsing

wetscrubbingby scothbright

chromatingconcn- 0.8 -1.2Ph-1.2-2.0

hot air dryingtemp-100Deg C

coatingon back side30 wftvisco-30 -35sec

bakingas per g.p.thicknessPMT -224

water quenching

hot air dryingtemp -90-100 Deg C

additionalcold air drying

primer coatingwft-15-18visc-30 sec

bakingas per thickness

-

water quenching

hot air dryingtemp=100Deg C

additionalcold air drying (r.t.)

topcoatingwf t=55-65visco-58-65

water quenching

hot air dryingtemp-100 -110 Deg C



inspection

g/f application?

G/f

yes sampling&

testing

no

recoiling ?O.k

marking for next routeas per cust.reqmt.

transfer toconcernedline

despatchas reject

finishing asper reqmnt.(coil, sheet,profile)

packing despatch/shipping

testing of ph , concn. ,temp,of water , degreasingand chromating

yesrecordingproperties

bakingas per thicknesspmt-2242 burner

storage atcustomer end

guardfilmremoval

?Paint

peel off

complaintraising bycustomer

installation

yes

end

visit tocustomer for assesment

replacement /rework end

START

POINT

squeezing byrubber roll



squeezing byrubber

2 stagesqueezing byrubber roll

squeezingby rubber roll & CPC


squeezingby rubber roll

no

Make the process map aligned to your Projectobjective, Dont paste ISO process maps.

Another way of mapping processes CFPM


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Another way of mapping processes- CFPM

Teams can use this structure for processes that move across functions

Function 1

Function 2

Function 3

Function 4

Step 1

Step 2

Step 3

Step4

Step 5

Step 6 Step 7

Step 8 End

4.5, 4-6Hrs

1.2, 1-1.5Hrs

0.2, 0.1-0.5Hrs

See Cycle time also.Average time & Range

given

Process map is must in cycle time reduction projects

Points to Look For


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Points to Look For

The pain areas (identified at the time of project selection) must be within the

selected scope

Guard against analyzing the process at this stage, just map as-it-is

Do not map the process as you would like it to be

Take care of Parallel & Sequential activities for Cycle time reduction projects


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Define what to measure and how to measure

Why Collect Data?


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Why Collect Data?

Improvement can only occur if we understand where we are & where to go, supported

by a measurement system that validates both situations

Successful organizations have a common language to communicate-- Data

Common language- Data, promotes objectivity in decision-making process

Have you reached where you intended to? -- only data answers that question

Its rightly said If you cant Measure it, you cant improve it.

Develop Operational Definition


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Develop Operational Definition

An Operational Definition is a precise description that tells how to get a value for the

characteristic you are trying to measure. It includes what something is and how tomeasure it

Purpose:

Removes ambiguity common understanding of defect definition

Identifies what to measure

Identifies how to measure it

Aim is to have apple to apple comparison forBaseline performance and improved performance.

Operational Definition Examples


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Operational Definition - Examples

Try to Remove ambiguity from the statements

Im coming by 8 o clock.verify 8 am or pm

Im reaching Dammam by 8 o clock flight.verify 8 o clock is Arrival or Departure time

Im waiting for Doctor for so longverify 5 minute or 5 Hrs

Give me half bottle water---bottle may by 200ml to 20 Lt

Ive reduce patients waiting timeat which stage or what is the start & end point

Ive improve profitabilityin which region/ which product

Operational Definition & Data collection plan


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Operational Definition & Data collection plan

Operational DefinitionUnit of Measurement

What to Measure How to Measure

Performancemeasure (Y) Operationaldefinition Data sourceandlocation

Samplesize

Who willcollect the

data

When willdata becollected

How willdata becollected

Other data thatshould becollected at the

same time

Operation Definition - Example


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Operation Definition Example

Objective: Why we need this/ Biz case

Scope/ coverage: Reasons if not 100%

Clarify what we are measuring: Uptime/ Response/ Resolution time, etcIs it aligned with the customer reqt (SLA/ Contract)

Source of Data: IS System (Pure), Manual, Hybrid (IS System with manualintervention )

Sampling plan/ Freq of data collection

Process owner

Any stratification reqd/ recommended: SLA w.r.t. Skill calls, spare calls, EC Vs FE

Challenges in authenticity of data: Like RE Call closure may not reflect 100%

Exp.: SLA Calculation at A/c level

Types of Data


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Types of Data

Discrete data

Data that can take a limited number of values (Pass / Fail, OK / Not OK, Win / Loss)

Examples

Days in a week

Number of yes responses to a satisfaction survey

Number of countries that play cricket

Continuous Data

Data that be expressed in either fractions or whole numbers

Examples

Temperature of the room

Exchange rate of a currency

Yield of a process

Height of a person

You need to be careful while selecting thedata type and you need to follow the sameapproach in improve phase also.

Which Type of Data is Preferable?


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Which Type of Data is Preferable?

Large sample sizes are required to measure higher Sigma multiples for discrete data

Since continuous data measurements can be broken down, relatively smaller

samples are required for higher Sigma calculations

Discrete data does not allow to understand the process variation

How Good is Good and How bad is Bad.

8:30 AM

A B C D

11:00 AM


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Design Sampling Plan for Establishing Process Baseline


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Introduction to Sampling


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Introduction to Sampling

We do sampling all the time

Populations & SamplesPractical aspects Cost & Time

Sampling is done to study a representative portion of population

Any term describing the characteristics of a sample is called statistic

Any term describing the characteristics of a population is called parameter

Population

Sample

S am

pl i n

g

Tool

How Big a Sample?


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Too Big:

Requires toomany resources

Too small:

Wont do the job

g p

Take BB Assistance for calculating Sample Size


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D

M AIC

ValidateMeasurement System

for Y

Validate Measurement System for Y


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y

In transactional environment a robustOperational definition will ensure Good

measurement system

Use ITIL guidelines & Industry bestpractices for arriving at right Operation

definition ( e.g. AHT, FCR etc )

Take BB help in designing & performing GR&R

Gage Repeatability & Reproducibility


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DM

AIC

EstablishProcessBaseline


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Compute Process Sigma Multiple UsingCollected Data

Six Sigma Metrics


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g

Customer Specification / Unit / Defect Definition

Discrete Data Continuous Data

Defects

Defects per Unit(DPU)

Defects per Million Opportunities(DPMO)

Mean

Std. deviation

Process Sigma Multiple - Z

1

2a 2b

3


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Process Sigma Multiple for Discrete Data

Business Problem


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A project team wants to reduce the total waiting time taken in hospital (from reception

to discharge). Team has randomly collected the following data of last 3 weeks for thewaiting time for few patients/ customers :--

(Data given is in minutes)

USL decided by Management based on other hospitals study is decided at 60 Minutes max.

Customer Specification / Unit / Defect1


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p

Unit: Any customer/ patient coming for treatment

Customer Specifications: USL is 60 minutes

Defect Definition: Any customer whose total waiting time exceeds 60 minutes

USL: Upper Specification Limit for a Performance Standard. Anything above this is a defect.

Discrete Data2a


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Lets treat this data as discrete

That means I would count the number of patients who had to wait more than 60minutes

There are only 2 defects in 30 units

Customer 1 5 Customer 11 50 Customer 21 49Customer 2 49 Customer 12 48 Customer 22 48Customer 3 48 Customer 13 36 Customer 23 39

Customer 4 53 Customer 14 50 Customer 24 49Customer 5 58 Customer 15 50 Customer 25 34Customer 6 50 Customer 16 62 Customer 26 33Customer 7 46 Customer 17 45 Customer 27 57Customer 8 50 Customer 18 47 Customer 28 48Customer 9 49 Customer 19 51 Customer 29 47Customer 10 47 Customer 20 44 Customer 30 390

Defects per Unit2a


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So, DPU of the given data is = 2 / 30 = 0.067

DPU =Number of defects found at any check-point

Number of units processed at that check-point

Sigma Multiple Calculation Discrete Data 3


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Every DPMO value relates to a particular Sigma Multiple or Z ST value. In this case, this

process is working at 3.00 Sigma multipleThe same can be calculated by Given Excel template also.

ZST ZST


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Process Sigma Multiple for Continuous Data

Continuous Data2b


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Lets treat the data of the waiting time data as Continuous and calculate Base Line

Sigma

For calculating Baseline or Sigma Multiple ZST in continuous data, we need tounderstand: Mean, std deviation & shape (Type of Distribution)

Customer 1 5 Customer 11 50 Customer 21 49

Customer 2 49 Customer 12 48 Customer 22 48Customer 3 48 Customer 13 36 Customer 23 39Customer 4 53 Customer 14 50 Customer 24 49Customer 5 58 Customer 15 50 Customer 25 34Customer 6 50 Customer 16 62 Customer 26 33Customer 7 46 Customer 17 45 Customer 27 57Customer 8 50 Customer 18 47 Customer 28 48Customer 9 49 Customer 19 51 Customer 29 47Customer 10 47 Customer 20 44 Customer 30 390

Sigma Multiple Calculation Continuous Data 3


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In this case, Mean = 57.73 minutes

Std dev : 63.54

USL : 60 Minutes

ZST can be calculated by the Excel Template.

This process is working at 1.54 Sigma multiple ( Z ST = 1.54, or DPMO = 484750).

Can you notice the difference in Z ST & DPMO values by treating the same data in

different way. What is the message ?


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Introduction of Normal Distribution

Introduction to Normal Distribution


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Its a Probability Distribution, illustrated as N ( , )

Simply put, a probability distribution is a theoretical frequency distribution

Higher frequency of values around the mean & lesser & lesser at values away from mean

Continuous & symmetrical

Tails asymptotic to X-axis

Bell shaped

Total area under the Normal curve = 1

100 110 120 130908070

Figure 3.01

1 unitof

standarddeviation

+-

Normal Distribution withMean =100

Standard Deviation = 10

Prediction based on Normal Distribution


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Figure 3.02

+-

- 1 + 1

95.46%- 2 + 2

68.26%

- 3 + 3 99.73%

+ 4 99.9937%

- 599.99943%

+ 5

- 6 + 6 99.999998%

- 4


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DM

AIC

DefinePerformance

Goals

Improvement Paths in Process Sigma Multiple


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Mutually signed-off change in specification limits

Its not a process improvement

Shifting the mean

Reducing the variance


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Benchmarking Exercise

Benchmarking


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Benchmarking is about looking at others for superior methods

Others may include suppliers, competitors, customers or even a different industry

Benchmarking could be done both on means as well as ends

Types of benchmarking

Internal

Functional

Competitive

B en c h m

ar k i n

g

Tool


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Establish Process Improvement Goal p-value &confidence band concept


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Means & Ends of a Process


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Y = f ( X1, X2, X3X n )

Dependent

Process Output

Effect

Symptom

Monitor

Independent

Process Input / Step

Cause

Problem

Control

Y X

Generating & Prioritizing Xs


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Identifying & prioritizing Xs could be done using both non -statistical & statistical tools

Identification tools include (non-statistical basis)

Experience of process doers

Brainstorming & Multi-voting

Process mapping

Fishbone

Prioritization tools include

Pareto Diagram

Stratification

Correlation (for a single X)

Regression (for multiple Xs)

Chi-square test

ANOVA

Failure Mode & Effect Analysis (FMEA)

Focus on identifying problem areas

Focus on identifying potential root causes

Focus on prioritizing potential root causes

Identification Tools


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Brainstorming & Multi-voting is a structured methodology to collect responses from a

group & arrive at a short-listEven though it provides a short- list, its not an effective prioritization tool because short -listing

is not done on any mathematical basis

Use of Process mapping requires studying the micro as-is process mapped in step 2

earlier & walking through it

Fishbone relates an effect to its cause by drawing a tree diagram

Points to Remember in Brainstorming


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All ideas are important, dont out rightly reject any idea

Participation should be ensured from all team members

To ensure this, project teams could use the round-robin method of idea generation

Its advised to use the Black Belt as the facilitator here

Fishbone


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Also called Cause & Effect or Ishikawa diagram

Focus of Fishbone is to arrive at the root causes of the problem areas identified

through multi-voting / process mapping

It works on the principal of asking why to each cause till you reach the root cause

F i s h

b on

e

Tool

Characteristics of a Fishbone


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Characteristics of a Fishbone


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Search for the REALCAUSES of the problemsthat you deal with.

How to Construct a Fishbone


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Review the problem statement / defect definition

Brainstorm & identify possible causes

Sort causes into reasonable clusters

Choose the clusters & name them into big bones

Check the logical flow of cause and effect

Fishbone Structure


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Effect

Cause 1

Cause 11

Cause 12

Cause 21Cause 22

Root-cause221

Root-cause222

Cause 31

Big Bone Medium Bone

Backbone

Cause 2

Cause 3

Hows for 1stHouse

Fishbone Structure


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Effect

Cause 1

Cause 11

Cause 12

Cause 21Cause 22

Root-cause221

Root-cause222

Cause 31


Backbone

Cause 2

Cause 3

Hows for 2ndHouse

Fishbone Structure


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Effect

Cause 1

Cause 11

Cause 12

Cause 21Cause 22

Root-cause221

Root-cause222

Cause 31


Backbone

Cause 2

Cause 3

Hows for 3rdHouse

Fishbone Example


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Wrong CustomerDetails

CRM malfunction Error in Listening

Foul order

Printer error

CRM not saving thedata properly

Lack of trainingon listening skills Problem with

instrument

Nomaintenance

Roughhandling

No credit card verification

Root-cause 1

Backbone

No cross-check

Root-cause 2

Fishbone can be used in organizing the causes also.

Key Concepts


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Its difficult to judge how many levels one needs to explore in a fishbone

Sometimes project teams may hit the root-cause at multi-voting / process mappinglevel, but they may do a further root-cause for the sake of it

In such cases, it is always a good idea to see if there is further scope for exploring byasking why

Always remember that we are trying to identify controllable Xs , what can not

be controlled can not be a root-cause

One must be able to see the effect on Y when values / levels of root -causes are

changed


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Separate Vital Few from TrivialMany for Further Screening

Prioritization Tools


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Some teams may do a multi-voting here, at the output of root-cause analysis

Pareto diagram works on the 80:20 rule of 20% causes contributing to 80% of defects

Co-relation & Regression help in identifying the movement of continuous Y withrespect to continuous X

ANOVA & Chi-square help in identifying the movement of continuous / discrete Ywith respect to discrete X

Failure Mode & Effect Analysis (FMEA) identifies the process failure modes &

assigns a number to it that helps to prioritize the actionables

Success of all prioritization tools depends upon data collection

Pareto Diagram


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A graphical tool for ranking causes form most significant (Vital Few) to least significant (Trivial many).

Based on Pareto principle, which was first defined by J.M Juran in 1950. Pareto principle was madeafter 19th century Italian economist V. Pareto (1897). Most effects come from relatively few causes.

Also known as 80:20 Principle .

Pareto diagram indicates which area should be taken up first in eliminating defects and improvingoperations.

Example: Suppose a person identifies multiple root-causes of reaching his office late.Now he is not sure where to focus so that he reduces the occurrence of

reaching late by minimum 50%.

He has identified following root causes

Woke up late

Clothes not ready

Breakfast not readyBus not coming on time

Traffic jam

Bus waiting for other employees

He collects data on how frequent each of the root cause is & constructs a Pareto

P ar e

t o

Tool

Pareto Diagram


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B u s n o t c o m

i n g o n t i m

e

C l o t h e s n o t

r e a d y

W o k e u p

l a t e

T r a f f i c

j a m

B r e a k f a s t n o t

r e a d y

B u s w

a i t i n g

25 18 15 6 5 235.2 25.4 21.1 8.5 7.0 2.835.2 60.6 81.7 90.1 97.2 100.0

0

10

2030

40

50

60

70

0

20

40

60

80

100

CountPercentCum %

P

e r c e n t

C o u n t

Frequencies of root causes for reaching office late

Other Prioritization tools


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So far, we have not used any statistical tool to prioritize Xs.

Depending upon the data characteristics of Y & X, we can choose the appropriate tool

Correlation&

Regression ANOVA

Chi-square

Continuous

Continuous

Discrete

Discrete

Y

X

Identifyopportunities for

converting Yinto a continuousone or use FMEA

Regression


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While correlation tells us only about the direction of movement, it does not

throw much light on degree of movement in one variable with respect tomovement in another

Regression of Y on X results in a transfer function equation that can be used to

predict the value of Y for given values of X

Y can be regressed on one or more Xs simultaneously

Simple linear regression is for one X

Multiple linear regression is for more than one Xs

R e gr e

s s i on

Tool

Y = f ( X )

Multiple Linear Regression (MLR)


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Multiple regression regresses Y on more than one Xs simultaneously

It is similar to Two-way ANOVA we have discussed in step 5, except for the differencethat Xs used in ANOVA were discrete

The approach is similar & a linear multiple regression equation looks as follows:

where Y = Dependent variable / output / response

X1 = First independent variable / input / predictor

X2 = Second independent variable / input / predictor

A = Intercept of fitted line on Y axis

B1 & B2 = Regression coefficients / Slopes of the fitted plane on two axes

C = Error in the model

Y = A + B1X1 + B2X2 + + BnXn + C

Multiple Regression Example


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Suppose we are trying to predict rent of an apartment based on the size of theapartment & its distance from the main commercial area. We gather the followinginformation as below. If you are looking for a two-bedroom apartment 2 miles from themain area, what rent should you expect to pay?

X1Y X2

Multiple Regression Example


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Minitab gives the following output:

For a 2 bedroom apartment 2 miles away from main area, expected rent could be INR3614 per month

Regression Analysis

The regression equation is

Rent = 852 + 1381 * Rooms

Predictor Coef StDev T P

Constant 852 1146 0.74 0.511

Rooms 1381.0 259.5 5.32 0.013

Distance -5.4 142.1 -0.04 0.972

S = 975.3 R-Sq = 91.5% R-Sq(adj) = 85.8%

Analysis of Variance

Source DF SS MS F P

Regression 2 30665301 15332650 16.12 0.025

Residual Error 3 2853449 951150

Total 5 33518750

ANOVA


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ANOVA is used to short- list potential discrete Xs for a continuous Y

We can use one- way ANOVA & see the variation in Y for one X at a time

We can use two-way ANOVA for more than one X

A N

OV A

Tool

ANOVA Example


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A team puts great emphasis on customer satisfaction. For some weeks, the ratingsseemed to suffer & the manager tried to identify the factors that could be causing this.He chooses two of the potential discrete factors as Team that handles the call & Shiftin terms of A & B. Which factor is vital?

Team Shift Rating

Desktop A 3.4DC A 4.5




Desktop A 3.1DC A 5

Desktop B 3.5

DC B 4.2Desktop B 4.6DC B 4.9

Desktop B 3.5DC B 4.1




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Failure Mode & Effect Analysis (FMEA)


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FMEA is a simple tool to prioritize the failure modes & actions

By understanding why and how we fail, we can plan for success

It works on the belief that proactiveness saves time

Typically, FMEA is applied on the output of root-cause analysis, & is a better tool for focus / prioritization as compared to multi-voting

We shall focus on Process FMEA (Design FMEA is used in designing products)

F ME A

Tool

FMEA Concept & Output


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Risk Priority Number (RPN) = S * O * D

Severity, Occurrence & Detectability are measured on a scale of 1-10

FailureMode

Effect

Cause

Control

Process / Product

Characteristics

S everity

Occurrence

Detectability

Risk

Priority

Number

Action

Plan

FMEA Concept & Output


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Process / Product characteristics - Purpose of the product or Process

Failure Mode - How can the product / process fail to function?

Effects - Which effects are most severe to customer?

Causes - Which causes are most likely to occur?

Controls - Ability for current controls to detect causes?

RPN - Which high risk cause we work on first?

Action Plan - Recommended actions & responsibilities

Scales of SOD - Severity


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Severity is the seriousness of the effect of the failure mode on the customer

Rating Scale:

Scales of SOD - Occurrence


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Occurrence is the probability that a specific cause will result in the particular failure mode

Rating Scale:

Scales of SOD - Detectability


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Detectability is the probability that a particular cause will be detected

Rating Scale:

FMEA Table


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I


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DMAICExplorePotentialCauses


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Explore Potential Xs for Causation

Recall Step 6: Creating a List of Xs & Prioritization


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x1x2x3x4x5x6x7x8x9x10x11

x12

x7 = 38%x6 = 27%x2 = 12%

x9 = 4%x10= 4%x5 = 2%x1x3x4x8 = 13%

x11x12

Exploration of the y-x

relationship

Vital Xs

Trivial Xs

Brainstorming

FMEA

Fishbone

Regression, FMEA

error

I


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Design Counter-Measures

DMAIC

Counter-Measure Matrix


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Counter Measure Matrix is used to arrive at action to be taken on a root cause thatwould lead to the desired result

If there are more than one counter-measure for a root cause, then the counter measure is prioritized using three parameters

Each counter-measure is given a rating on a scale of 1-7 (higher the better) & overallscore is calculated by multiplying the 3 ratings, one with the best score is chosen

C o un

t er -M

e a s ur eM

a t r i x

Tool

Counter-Measure Matrix - Example


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Cost optimisation levers CCOP

Service Desk IR

Automation


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Map the New Process

DMAIC


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Develop Control Mechanism

DMAIC

Controlling the Xs


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So far, we have identified the best settings for each of the vital X

The key now is to ensure that the Xs dont vary away from the targeted setting

Process control is a crucial tool in ensuring that this Six Sigma project delivers lastingbenefits

Maintaining Xs at their target level can be done in two ways

Detection (reactive) Why was change not detected?

Prevention (proactive) Why did / would change occur?

Correction Loop


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Prevention

Mistake-Proofing Statistical ProcessControl

Prevention&

Detection

Mistake-Proofing (Poka-Yoke)


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It is a technique to make errors difficult to happen, if not impossible

Ground Rules of Mistake Proofing

Intelligence does not depend upon hierarchy

Target tasks that require constant alert / vigilance / memory

Look for cutting unproductive time to foster creativity

Defects are not acceptable, in whatsoever small number

It is best to make it impossible to do it wrong

Mi

s t ak

e-P r o

of i n

g

Tool

Advantages of Mistake-Proofing


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Easy to accomplish without any formal training

Removes repetitive tasks

Fosters creativity & value-addition

Ensures less defects

Mistake-Proofing Techniques - Examples


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Technique Prevention Detection

SHUTDOWN Cameras that dont clickif the shutter is on Air-conditioners trip whendetect slight over-heating

CONTROL ATM does not accept acard inserted wronglyQuality check at eachpoint in the assembly

WARNING Car security systemalerting the driver that alldoors are not closed

Smoke detectors

If Mistake-Proofing is Not Possible?


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Provide guidelines

Check-lists

SOPs

Templates

Use visuals

Color-codes

Shapes

Statistical Process Control (SPC)


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SPC was developed by Walter A. Shewhart in 1924

Historically, SPC has been used to monitor & control output Y

In DMAIC, we apply SPC to control Xs (remember Y is only monitored)

However, sometimes applying SPC to Y can also be beneficial in detecting trends

About SPC

Aids visual monitoring & controlling

Depends heavily on data collection

S t a t i s t i c al P r o

c e s s C on

t r ol

Tool

Basics of Control Charts


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Control charts are useful for tracking process statistics over time and detecting thepresence of special causes

A process is in control when most of the points fall within the bounds of the controllimits, and the points do not display any nonrandom patterns

Purpose of Control Limits


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LCL

UCL

Special Cause Variation

Special Cause Variation

C o m m o n

C a u s e

V a r i a

t i o n

CL

Process Control is inherent to process characteristics as against Process Capability

which is measured as per outside targets & specifications

Purpose of Control Limits


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UCL = + 3

LCL = - 3

99.73%

0.135%

0.135%

Control Limits define a probabilistic levelof occurrence of an out of control point

Out of control point

CL

Do Not Over-react to Change


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UCL

LCL

L o a

d i n g

t i m e

Excellent Work Change the transporter??

Distinguish between Noise & Change

Top Eight Indications of an Out of Control Process


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1. A single point outside 3 control limits

2. Nine points in a row on the same side of the centerline3. Six points in a row, all increasing or all decreasing

4. Fourteen points in a row, alternating up and down

5. Two out of three successive points more than 2 on the same side of the

centerline6. Four out of five successive points beyond 1 on the same side of the centerline

7. Fifteen points in a row within 1 on either side of center line

8. Eight points in a row with none between 1 on either side of center line

Choosing An Appropriate Control Chart


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Continuous Data

Individual Data Points Subgroups

Pulling one sample at fixed frequency Taking periodic grouped data

I & MR Variability of individualcharacteristics over time X & RVariability of average

characteristics over time

Choosing An Appropriate Control Chart


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Discrete Data

Defectives Defects

P-chart U - chart

Rest of the Control Charts are in the BB training Scope


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Continuous data Control Charts

I & MR Control Chart


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Average Handle Time (AHT) by an agent is a vital X for productivity. This is monitoredperiodically to keep it within desired operating range.

Data on AHT is collected over several hoursHour AHT ( sec )

1 65

2 69

3 67

4 66

5 63

6 70

7 71

8 68

9 64

10 69

11 63

12 68

13 84

14 81

15 68

I & MR Control Chart : Minitab Output



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Observation

I n

d i v i d u a

l V a

l u e

151413121110987654321

80

70

60

_ X=69.07

UC L=82.93

LC L=55.20

Observation

M o v i n g

R a n g e

151413121110987654321

16

12

8

4

0

__ MR=5.21

UC L=17.04

LCL=0

5

1

I-MR Chart of TemperatureI & MR Chart of AHT


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Discrete data Control Charts

P Control Chart


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A call center receiving calls will have to closed within specified SLA. Team hascollected data every hour to monitor process control

Defective is defined as call not closed within SLA

P Control Chart : Minitab Output


P Chart of P defectives


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Sample

P r o p o r t i o n

24222018161412108642

0.4

0.3

0.2

0.1

0.0

_ P=0.0944

UCL=0.2906

LCL=0

P Chart of P defectives

Tests performed with unequal sample sizes

Points to Remember in Control Charts


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Do not apply SPC tools to processes that are known to be out on control

Do not compare control limits with specification limits

Do not ignore out -of-control signals if your Y is meeting the specifications & X ismeeting the operating limits

Ensure that observations are independent of each other

Its quite possible that X is under control, but Y is out -of-specs


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Re-compute Process Baseline & Verify AgainstTarget

Validating Process Improvements

Two Sample T Test and CI: Cost / Indent Status (Control Phase)


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Two-Sample T-Test and CI: Cost / Indent, Status (Control Phase)

Two-sample T for Cost / IndentSE

Status N Mean StDev MeanBefore 7 300.7 47.6 18Improved 7 154.1 61.6 23

Difference = mu (Before) - mu (Improved)Estimate for difference: 146.795% lower bound for difference: 93.8T-Test of difference = 0 (vs >): T-Value = 4.98 P-Value = 0.000 DF = 11

I-MR charts

( stage wise )


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How to Sustain Process Improvement


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Control Plan

Training & Communication

Project handovers with completed project dockets

Database of vital Xs

QFD / VOC

FMEA / Fishbone

Six Sigma Project Audits

Process Owners

SOPs

DMAIC SUMMARY: Problem Solving Flow


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BusinessProblem

StatisticalProblem

StatisticalSolution

BusinessSolution

MEASURE ANALYZE IMPROVE CONTROL

P R O J E C T

Six Sigma resource in Wipro Intranet


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Q City : http://qcity.wipro.com:8080/hive/index.jsp

Six Sigma resource in KM


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KM Link : http://knetsites.wipro.com/sites/1010/Pages/Default.aspx

Process Excellence Wipro Wayhttp://wiproway.wipro.com/default.asp


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WI MQ Mentor List

Name LOB Region Email id


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Sachin Agarwal [ MBB ] Services Common HO - BLR [email protected]

Jyoti Prakash [ LBB ] Services Common HO - BLR [email protected]

Priya Mahadevan [ LBB ] MIT HO - BLR [email protected]

Rashmi Salgar [ BB ] ES & SI HO - BLR [email protected]

Sumathi Rajesh [ BB ] Regional MQ S2 [email protected]

Bhakti Gokhale [ BB ] Regional MQ West [email protected]


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Improvement is a Journeyand Together We Will

Improvement is a Journeyand Together We Will

Thank You

Gb Training Dmaic

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